Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes

Abstract

Automated remote sensing has made substantial breakthroughs for archaeological investigation. Over the past 20 years, the reliability of these methods has vastly improved, and the total number of practitioners has been increasing. Nonetheless, much of the work conducted, to date, focuses almost exclusively on specific topographic features and monumental architecture, ignoring the potential of automation to readily assess more ephemeral components of the archaeological record. Likewise, the emphasis on specific feature types overlooks broader landscape patterns, thus delegating automated remote sensing analysis as a method in and of itself, mostly disconnected from larger archaeological and anthropological investigations. Here, I review recent attempts to rectify this shortcoming by using automated analysis methods to record and explain ephemeral archaeological material distributions. While such research is limited, I argue that the successes achieved in these recent studies offer a pathway forward for automated remote sensing to become more fully integrated with archaeological work beyond the detection of specific topographically distinct features.

Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Communities in the Southwestern Indian Ocean

“…While the potential of multispectral and hyperspectral imagery has long been established for archaeology, the detection of scant artifact scatters is not the norm (c.f., Orengo and Garcia-Molsosa, 2019). Rather, most literature focuses on the detection of highly visible landscape modifications, like architecture and remains of intensive agricultural activity (e.g., Tarolli et al, 2019; also see Davis, 2021). As such, this work suggests that the development of machine learning and cloud-based computational processing provides the ability to detect even the most ephemeral archaeological deposits and use these to reveal patterns of human activity and impact on the wider landscape.…”

“…Uses of remote sensing technology (i.e., satellite images) in such contexts have successfully identified these "low-impact" signatures of human action on large geographic scales (Lombardo and Prümers, 2010). While predictive models have been used around the world to narrow the search for ephemeral archaeological deposits (McMichael et al, 2014;Kirk et al, 2016;Davis et al, 2020a), the use of automated remote sensing in archaeology remains skewed toward landscape modifications that are easy to identify because of their lasting marks and effects (Tarolli et al, 2019;Davis, 2021).…”

Liberating trails and travel routes in Gitxsan and Wet'suwet'en Territories from the tyrannies of heritage resource management regimes

“…Third, the heavy and long-term use of trails can result in what Sheets and Server (1991) call troughing, potentially visible with LiDAR and other remote sensing tools. With expanding access to technology and LiDAR data, remote sensing in archaeology is moving away from its focus on topographically distinct features and monumental architecture and toward the identification of more subtle sites, including trails (e.g., Davis, 2021). Our remote sensing reconnaissance helped us target likely travel routes (e.g., linking through meadows) and other geological features (slope breaks, avalanche chutes).…”

Investigating ancient agricultural field systems in Sweden from airborne LIDAR data by using convolutional neural network

“…However, due to the complexity and scale of this dataset, there is a need for further study of automated analysis with different techniques since, as is also mentioned in the above studies, there are still difficulties with the visual surveying massive amounts of LIDAR data with subsequent processing and interpretation (Albrecht et al, 2019;Cowley, 2012;Freeland et al, 2016;Guyot et al, 2021;Lambers & Traviglia, 2016;Rączkowski, 2020;Sevara et al, 2016;Somrak et al, 2020;Trier et al, 2018;Verschoof-van der Vaart & Lambers, 2021). Recently, automated and semiautomated techniques using artificial intelligence (AI)-based approaches have found their place in the field of archaeological remote sensing (Davis, 2021;Lambers et al, 2019;Olivier & Verschoof-van der Vaart, 2021). As compared to applications on LIDAR dataset, AI applications on satellite and unmanned aerial vehicle (UAV)-based imagery are scarce and they are based on employment of convolutional neural networks (CNNs), random forests (RFs) and YOLO classifiers for the detection of burial or settlement mounds (Caspari & Crespo, 2019;Orengo et al, 2020) or other archaeological features (Berganzo-Besga et al, 2021;Orengo & Garcia-Molsosa, 2019).…”